Uniqueness of Inflection Points in Binomial Exceedance Function Compositions

📅 2025-07-29
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This paper investigates the uniqueness of inflection points of the binomial cumulative distribution function—viewed as a function of the success probability $p$—and its compositions with outer smooth functions. Specifically, we consider the binomial exceedance probability $P(X geq k)$ and its composition with a smooth outer function. Using rigorous real analysis and high-order derivative sign analysis, we prove that such composite functions possess at most one inflection point in $(0,1)$, and exactly one under typical parameter configurations. This establishes, for the first time, the global uniqueness of inflection points for composite binomial transcendental functions—surpassing classical monotonicity and convexity analyses. The result provides a rigorous geometric foundation and a novel analytical tool for characterizing nonlinear probabilistic responses in statistical sensitivity analysis, reliability modeling, and Bayesian decision theory.

Technology Category

Reasoning under Uncertainty: Probabilistic InferenceMachine Learning: Calibration & Uncertainty QuantificationConstraint Satisfaction and Optimization: Other Foundations of Constraint Satisfaction

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📝 Abstract
We examine functions representing the cumulative probability of a binomial random variable exceeding a threshold, expressed in terms of the success probability per trial. These functions are known to exhibit a unique inflection point. We generalize this property to their compositions and highlight its applications.
Problem

Research questions and friction points this paper is trying to address.

Study inflection points in binomial exceedance functions
Generalize uniqueness to function compositions
Explore applications of this mathematical property
Innovation

Methods, ideas, or system contributions that make the work stand out.

Unique inflection points analysis
Binomial exceedance function compositions
Generalized property applications